Arbitrary absolute vs. individualized running speed thresholds in team sports: A scoping review with evidence gap map

The aims of this scoping review were (i) to characterize the main methodological approaches to assessing individualized running speed thresholds in team sports players; (ii) to assess the use of traditional arbitrary (absolute) thresholds compared to individualized running speed thresholds in team sports players; (iii) to provide an evidence gap map (EGM) about the approaches and study designs employed in investigations in team sports and (iv) to provide directions for future research and practical applications for the strength and conditioning field. Methods studies were searched for in the following databases: (i) PubMed; (ii) Scopus; (iii) SPORTDiscus and (iv) Web of Science. The search was conducted on 15/07/2022. Risk of bias was assessed using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). From 3,195 potentially relevant articles, 36 were eligible for inclusion in this review. Of the 36 included articles, 27 (75%) focused on the use of arbitrary and individualized running speed thresholds to describe the locomotor demands (e.g., high intensity running) of players. Thirty-four articles used individualized speed running thresholds based on physical fitness assessments (e.g., 40-m linear sprint) or physical performance (e.g., maximal acceleration). This scoping review supported the need for a greater focus to be placed on improving the methodological aspects of using individualized speed running thresholds in team sports. More than just creating alternatives to arbitrary thresholds, it is essential to increase the replicability of methodological conditions whilst ensuring that research comparing the most adequate measures and approaches to individualization takes into consideration the population and context of each study.


INTRODUCTION
Monitoring the locomotor demands of team sport players during training sessions and matches is a common practice by coaches and has become a popular research topic over the last decade [1][2][3].
The evolution of microelectromechanical systems such as Global Positioning Systems (GPS), Local Position Systems, Ultrawide Band or inertial measurement units have facilitated accurate evaluations of the locomotor demands that are placed on team sport players [4][5][6]. This evolution has enabled coaches and researchers alike to characterize both the volume and intensity that players must sustain during training and competition [7,8]. The importance of Arbitrary absolute vs. individualized running speed thresholds in team sports: A scoping review with evidence gap map researchers and practitioners to define the next steps in standardizing thresholds within and between sports. To date, no scoping review has been conducted on the body of literature relating to individualized running speed thresholds in team sport players. Such a review is necessary in terms of mapping the extant literature and facilitating an evaluation of the landscape of the methodological approaches. Accordingly, the purposes of this study are to: (i) characterize the main methodological approaches to assessing individualized running speed thresholds in team sports players; (ii) assess the use of traditional arbitrary (absolute) thresholds compared to individualized speed thresholds in team sports players; (iii) provide an evidence gap map (EGM) on the approaches and study designs adopted in team sports; and (iv) provide future directions for research and practical applications for the sports science field.

MATERIALS AND METHODS
This scoping review followed the PRISMA 2020 guidelines [28] and took into consideration the recommendations for scoping reviews checklist (PRISMA-ScR) [29].

Protocol and registration
The scoping review protocol was preliminarily submitted and pub-

Eligibility criteria
Studies published in peer-reviewed journals, including those with the status of "in press" or "ahead-of-print", were considered. No date limitations were set, and studies undertaken in all languages were considered [30]. The eligibility criteria were established based on the PECOS (population, exposure, comparator, outcome, study design) approach: (i) population: team sports players, of any age, male or female, who were integrated into team training routines (i.e., not injured or with any reported pathology or health problems). Excluded were disabled athletes or those competing in adapted sports. (ii) exposure: exposed to analysis of individualized running speed thresholds in training sessions and/or matches; (iii) comparator: exposed to traditional arbitrary running speed thresholds in training sessions and/or matches; (iv) outcome(s): the time and/or distance and/or percentage of time and/or distance spent in different running speed thresholds (either in arbitrary/absolute or individualized thresholds); (v) study design: observational studies or interventions (both singlearm [if with two different metrics, for example, individualized vs. arbitrary] and multi-arm investigations were considered).

Information sources
The following databases were searched: PubMed, Scopus, SPORT-Discus and Web of Science (Core collection). After performing the variability in fitness levels, a recurrent practice in the monitoring of locomotor demands is the use of arbitrary (player-independent) running speed thresholds [12,13]. The use of arbitrary thresholds is often necessitated by software-based constraints that typically require fixed running speeds for analytical purposes. However, such thresholds are also required due to the methodological challenges associated with the individualization of running speeds which may vary from player to player and from sport to sport. Whilst arbitrary absolute running speed thresholds may allow coaches to benchmark players' values (across different contexts) and simplify the data monitoring process, they may also impede the individualization of training prescription because running speed can be physical fitness and context-dependent [14]. Moreover, although using arbitrary running speed thresholds has become common practice, these thresholds are not consistent across measurement instruments and contexts thus making it very difficult to summarize evidence in this domain [15].
The use of individualized running speed thresholds has been proposed as a way of overcoming the weaknesses associated with arbitrary thresholds [16]. This process is based on the physical fitness level of the individual player, aiming to mitigate between-player variability through the identification of a unique running speed threshold [17]. However, the selection of appropriate methods of individualization based on physical fitness levels represents a primary challenge that has been observed in the literature [18]. Different approaches have been utilized with individualized thresholds being based on maximal aerobic speed (MAS) [11], maximal sprint speed (MSS) [19] and anaerobic speed reserve (ASR) [14]. Besides the diversity of approaches used to establish individualized thresholds, other challenges have also emerged with, for example, the calculation of MAS being dependent on the type of test used to determine an athlete's performance [20]. On this, different tests used for establishing MAS (e.g., laboratory, field-based) have failed to find consistent agreement across analyses using gold-standard methods [21,22]. Moreover, the specificity of the test (e.g., distance-based or time-based) can affect the derived MAS value [20] and, still more, since ASR is MAS-dependent [20], the ASR method is similarly compromised. Using MSS can also be challenging due to minimal changes in performance occurring over the course of a season [23] and because sprinting threshold is affected by the biomechanical profile of the individual [13].
An alternative approach to the development of physical-fitnessbased thresholds is the use of a data analytics approach to defining running speed zones [24]. As an example, a study conducted in female soccer players used a k-mean, Gaussian mixture model and spectral clustering to define four running speed zones based on information extracted from players over 52 international soccer matches. In another example, spectral clustering was used to determine velocity thresholds in Gaelic football referees [25].
The diversity of approaches for defining running speed thresholds in team sport players is apparent in the extant literature [12,26,27].  abstracts of these articles were checked for relevant inclusion criteria and, if necessary, the full-text was referred to. Snowballing citation tracking, preferentially in Web of Science, was also conducted whilst two external experts (as recognized by Expertscape at the Worldwide level: https://expertscape.com/ex/soccer) were also consulted. Finally, errata and article retractions were analyzed for any articles that were included in the review [31].

Search strategy
In the search, the Boolean operators AND/OR were applied. No filters or limitations were used (e.g., date; language; study design) to maximize the chances of identification of appropriate studies [32]

Selection process
Two of the authors (HS and JA) independently screened the retrieved records (namely titles and abstracts). The same authors also independently screened the gathered full texts. Disagreements between the two authors were discussed in a joint reanalysis. In the case of no consensus being reached, a third author (FMC) made the final decision. Where and when required, all co-authors shared opinions

Web of Science
Search for title and abstract also includes keywords and its designated "topic" Context-related information: this included, but was not restricted to, period of the season, context of the assessment (period of rest before analysis, time of the day), the number of sessions/matches considered.
Methodological-related included the method used for the individualization (e.g., MAS, ASR, MSS) and the arbitrary/absolute running speed thresholds that were collected. It also included information about the instruments of measurement such as GPS, local positioning systems, or ultrawide band, and the regularity of the tests performed (if more than once).
Main outcome: considering the goal of executing a scoping review with an EGM, the main outcomes were those associated with the methodological approaches of the studies and not the specific results presented in each article. Accordingly, running speed thresholds were the variable of interest.
Additional information: this included, but was not limited to, citation details, year, country of data collection, funding sources, and competing interests.

Study risk of bias assessment
The risk of bias was independently assessed by two authors (JA and HS

Data extraction process
The data extraction process was firstly performed by the lead author (FMC) and was verified by two co-authors (RRC and HS) to confirm the accuracy and details of the data. A specially designed Microsoft ® Excel datasheet was created and used to contain the data and the main information. The Excel datasheet can be observed in the supplementary material. In the case of relevant data being missing from a full text of a study, the primary author (FMC) directly contacted the corresponding author of that study by email and/or ResearchGate to obtain the required information.

Data items
The descriptive characteristics of participants that were collected for Nonrandomized Studies (RoBANS) was used to assess the risk of bias of the included studies [34]. This scale has shown moderate reliability and good feasibility and validity [34]. The tool comprises six domains: the selection of participants; confounding variables; the measurement of exposure; the blinding of the outcome assessments; incomplete outcome data; and selective outcome reporting.

Data management and synthesis methods
An EGM was built to graphically represent the type of studies and the evidence collected on the main topic of research. The EGM summarized the findings and provided a brief overview of the evidence and research gap [35][36][37]. A narrative review also accompanied the results, while specific information about the number and/or percentage of studies and the topics of interest was outlined. Table 1 presents an example how information was collected regarding the scoping review context and outcomes.

Study identification and selection
The initial search yielded a total of 3,195 titles ( Figure 1).

Methodological quality
With regard to risk of bias, confounding variables were unclear in

FIG. 2.
Distribution of the included studies per continent, age-group and team sport.

Methodological characteristics of the included studies
High-intensity acceleration (> 75% maximal acceleration).
Martínez-Cabrera et al. [50] Compare arbitrary and individualized speed thresholds on high-intensity acceleration in match  Compare arbitrary and individualized speed thresholds on locomotor demands in matches Peak speed attained in matches.
The peak speed attained in matches was considered as the MSS.
High speed running (5 m/s 2 ). High speed running (5 m/s 2 divided by the average of MSS of the group).
Scanlan et al. [83] Compare arbitrary and individualized PlayerLoad thresholds in training sessions.
Peak instantaneous PlayerLoad intensity recorded in training.
The peak instantaneous PlayerLoad intensity was used to individualized threshold.

Method of individualization Arbitrary thresholds used Individualized thresholds used
Scott et al. [17] Determine dose-response relationship between locomotor and physiological demands while use arbitrary and individualized speed thresholds in training sessions. Siegle et al. [54] Analyze inter-individual differences in the locomotor speed and compare with general approach in match. To assess the relationships between external and internal load ratios, while considering arbitrary and individualized speed thresholds.

Incremental treadmill test
The velocities at 2 mmol/L and 4 mmol/L lactate were estimated to individualization of speed thresholds.   indicating that it remains difficult to undertake observational studies in international level or world class athletes. More research should focus on the elite level of sport with studies on world-class female soccer players [24] and the finalists of the men's world cup [54] being excellent examples of this.
In Although several studies reported the characteristics of the participants such as body mass and height, most of the included articles did not report this information clearly making it difficult to fully evaluate the extant evidence and compare results for future research.
Likewise, although the requirement to protect sensitive or personal data is understandable, it is vital to indicate the sex of study participants as physical fitness and running speed thresholds may vary based on this characteristic [12]. This is also vital in terms of upholding the replicability of a given study. From the minority of studies which reported the sex of participants, men were more researched (n = 12) than women (n = 6).
linear sprint test was the only test used for assessing peak acceleration (n = 3). One of the notable trends found amongst the articles was the diversity of individualized approaches used, with few studies focusing on determining the best method of individualization or comparing different methods to define which might be the most appropriate. Hereafter, the discussion will center on the methodological characteristics and potential bias found during the review.

Participation characteristics
There was a lack of consensus on the use of terms (e.g., elite, professional) to describe study participants amongst the included studies.
Such terms help to provide relevant information about the competitive level of study participants, however, populations such as youth athletes are not easy to classify given their relative lack of experience and variation between countries and sports. This may also be applicable at the so-called "elite" level as professional status can relate to having competed in any one of several different tiers of varying playing standard [53]. In an effort to improve the standardization of athletes' competitive levels, we recommend that researchers follow the Participant Classification Framework [33] which categorizes players based on their level of practice, volume of training and ranking.
The organization of study participants into well-defined tiers may help to standardize information for the scientific community, leading to more accurate appraisals of studies such as those in the current scoping review and the subsequent development of more useful knowledge that can be transferred to practitioners. In this scoping review, we have attempted to classify study participants based on the aforementioned framework, however, in some cases, this was challenging because basic information, such as training and match frequency and hours of training per week, were not available in the gathered studies. More accurate information such as participant skill level or level of competition (tier of league structure, competitions in status (i.e., rest times, ratings of perceived exertion) [60] or the sequence of how the tests were conducted. Moreover, it is also important to emphasize the need to report the validity and reliability of the physical fitness tests employed in each study [61].

Physical tests -specificities and methodological considerations
In the gathered studies, it was apparent that the individualization process was fundamentally associated with the estimation of MSS or the analysis of a cardiorespiratory marker. The 40-m linear sprint was the most utilized test, featuring in 11 studies and this was followed by the 30-m linear sprint. The 40-m linear sprint appears to be an appropriate distance with which to identify MSS regardless of the sport in question [13,62]. Moreover, the validity and reliability of linear sprint tests are also very high [62].
Despite the above, some methodological issues were found dur-  [64]. So too can the height of photocells [65] and the distance between the foot and the first pair of photocells and we encourage researchers to control for and report these factors when conducting studies.
Although the use of a radar gun could be the most recommended approach, the major alternative method to using photocells in the included studies was GPS. Previous studies suggest that GPS with 10 Hz (the most widely used in the gathered articles) can provide valid and reliable information about a player's peak speed [66]; however, GPS can present some fluctuations in reliability level depending on the position of the device [67]. Accordingly, it is important to detail how a given GPS was used and, in the report, to highlight the accuracy and precision level for estimating peak speed.

Sample collected
A highly diversity body of data were collected from the included studies. As an example, training volume varied from a minimum of two sessions [11] to a maximum of 114 [56]. Similarly, the number of matches analyzed varied from one (single match) [54] to 52 (collected over three years) [24]. Aside from the substantial difference in the amount of data collected, other issues were also apparent. For example, the period of observation was unreported in six studies with the remaining typically failing to provide any additional contextualization such as the period of the season the data was collected in and the schedule of matches that the teams were exposed to. In future studies, we recommend that researchers accurately describe the period of observation with relevant dates and information on the specific period and the content of the training week(s). Such information can be added as a supplementary file to journal submissions.
Encouragingly, all the gathered studies reported on the brand and model of the instruments used to collect data with the accuracy and level of measurement precision also very well described.

Physical fitness assessment -context-related information
Most (34/36) of the included studies used physical fitness assessment or performance analysis elements to individualize speed running thresholds. However, a particularly vital methodological issue related to the lack of accuracy in reporting the regularity of these assessments. Most of the studies used more than a single game to analyze running speed thresholds, and the range of the observation periods varied from two weeks [39] to three years [24]. Information about the regularity of the assessments and, most particularly, the time between the assessments and the range of matches analyzed was surprisingly scarce in most of the articles. Some studies reported the exact time of assessment (e.g., start of the pre-season or the week before the matches being assessed) [44,52] and others detailed the regularity of assessment (e.g., six measurements in six weeks) [41,19]. The availability of more accurate timelines, such as figures or supplementary files with schedules, could make it easier to identify which matches were associated with each fitness assessment. For example, in the case of an assessment performed on, say, 30 th October, it could be questioned as to whether that assessment would be relevant to the six subsequent scheduled matches or to the three matches before and the three matches after 30 th October. This would be particularly relevant during periods of the season when congestion in the match schedule results in multiple games being played within a very short timeframe [57]. Questions such as these arise from studies' reporting processes which can compromise the replicability of the methods.
Another issue commonly considered to cause a risk of bias is the absence of information on players' personal habits at the time of the assessment. Studies tended not to report many important factors related to readiness and performance strategies such as players' sleep habits (i.e., number of hours and quality of sleep the night before assessment) [58], the composition of dietary intake [59], recovery physical quality. If this does not occur, theoretically, a player could lose sprinting speed and subsequent estimations may be inaccurate relative to their best potential performance.

Future research
In line with the article of Beato et al [13], the commentary of Drust [73] and the letter to the editor of Kavanagh and Carling [74], more effort is required to consolidate methodological approaches to the study of individualized running speed thresholds in team sports.
There Future research must focus on comparing different individualization approaches (e.g., using multiple measures combined or isolated), testing across different periods of time (between the assessment and utilization) and identifying the practical effects on workload and injury risk. Moreover, clarification of the debate on the use of physical fitness markers versus machine learning that uses standards based on players' match demands can also be focused on further.
Additionally, analysis of the impact of moderators and mediators such as time of the season, type of population and players' level of training experience must be also considered.

Definition of the thresholds based on physical fitness measures
Multiple different approaches to establish the individualized running speed thresholds were observed in the gathered studies. The most common were MSS, MAS and the respiratory compensation threshold. Additionally, ASR, maximal acceleration, and maximal player load were also used. As an alternative, Q-Q plots or machine learning algorithms were utilized. The methods mentioned above may extensively impact upon the variables in a typical match analysis such as high-intensity running distance, high-speed running distance, and sprinting distance.
The lack of definition in the approach to speed threshold individualization was apparent in the gathered studies. As an example, sprinting was classified as both > 90% [52] and > 80% [42,48] of MSS as estimated in a test, or in the peak speed observed during a training session or match. Currently it is unclear which of these standards constitutes a sprint action. One advantageous reason for using MSS is the lower level of variability that can be observed across time [69]. This stability gives the measure a level of consistency that may not be possible when using MAS.

Conflicts of interest
The authors declared no conflict of interest. whose status is based on measures of overall scientific impact, this may not ensure that all relevant articles were identified. We focused on scientific studies that included both individualized and non-individualized running speed thresholds which meant that some articles, which focused only on individualized running-speed, were not included in the review. However, our methodological approach was designed to conform to all aspects of the PRISMA statement which represents a progressive methodological step in relation to the execution of a traditional scoping review. Finally, we established a rationale for the presentation of our results which consisted of exploring the methodological approaches made by the original studies, and not explicitly focusing on the primary outcomes reported in these studies.